Intro to FreeSurfer Jargon Intro to FreeSurfer Jargon voxel - - PowerPoint PPT Presentation

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Intro to FreeSurfer Jargon Intro to FreeSurfer Jargon voxel - - PowerPoint PPT Presentation

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Intro to FreeSurfer Jargon Intro to FreeSurfer Jargon voxel surface volume vertex surface-based recon cortical, subcortical parcellation/segmentation registration, morph, deform, transforms (computing vs. resampling) Intro to FreeSurfer

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Intro to FreeSurfer Jargon

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Intro to FreeSurfer Jargon

voxel surface volume vertex surface-based recon cortical, subcortical parcellation/segmentation registration, morph, deform, transforms (computing vs. resampling)

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Intro to FreeSurfer Jargon

voxel

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Intro to FreeSurfer Jargon

surface

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Intro to FreeSurfer Jargon

surface

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Intro to FreeSurfer Jargon

vertex

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What FreeSurfer Does…

FreeSurfer creates computerized models of the brain from MRI data.

Input: T1-weighted (MPRAGE) 1mm3 resolution (.dcm) Output: Segmented & parcellated conformed volume (.mgz)

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Recon

“recon your data” …short for reconstruction …cortical surface reconstruction …shows up in command recon-all

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Recon

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Volumes

  • rig.mgz

T1.mgz brainmask.mgz wm.mgz filled.mgz (Subcortical Mass)

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Cortical vs. Subcortical GM

coronal sagittal

subcortical gm cortical gm

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Cortical vs. Subcortical GM

coronal sagittal

subcortical gm

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Parcellation vs. Segmentation

(subcortical) segmentation (cortical) parcellation

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Intro to FreeSurfer Jargon

voxel surface volume vertex surface-based recon cortical, subcortical parcellation/segmentation registration, morph, deform, transforms (computing vs. resampling)

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Registration

Goal: to find a common coordinate system for the input data sets Examples:  comparing different MRI images of the same individual (longitudinal scans, diffusion vs functional scans)  comparing MRI images of different individuals

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target

Inter-subject, uni-modal example

flirt 6 DOF subject flirt 9 DOF flirt 12 DOF

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Linear registration: 6, 9, 12 DOF

Flirt 6 DOF Flirt 9 DOF Flirt 12 DOF subject target

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Linear registration: 6, 9, 12 DOF

target subject Flirt 6 DOF Flirt 9 DOF Flirt 12 DOF

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Linear registration: 6, 9, 12 DOF

target subject Flirt 6 DOF Flirt 9 DOF Flirt 12 DOF

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Intra-subject, multi-modal example

before spatial alignment after spatial alignment

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before spatial alignment after spatial alignment

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before spatial alignment after spatial alignment

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Inter-subject non-linear example

target CVS reg

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Some registration vocabulary

 Input datasets:

 Fixed / template / target  Moving / subject

 Transformation models

 rigid  affine  nonlinear

 Objective / similarity functions  Applying the results

 deform, morph, resample, transform

 Interpolation types

 (tri)linear  nearest neighbor

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FreeSurfer Questions

Search for terms and answers to all your questions in the Glossary, FAQ,

  • r

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